Compensation for Matrix Effects in High-Dimensional Spectral Data Using Standard Addition

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• The standard addition method is crucial for compensating matrix effects in analytical chemistry.
• Traditional standard addition is limited with high-dimensional data like full spectra.
• Existing methods for spectral data require matrix composition and blank measurements, limiting their use.

Purpose of the Study:

• To develop a novel standard addition algorithm for high-dimensional data.
• To enable accurate analyte quantification without matrix composition or blank data.
• To address limitations of existing methods in complex matrices.

Main Methods:

• A new algorithm modifies experimental data (e.g., spectra) prior to chemometric modeling.
• The method is designed to function without requiring knowledge of matrix composition.
• It also operates without the need for blank measurements.

Main Results:

• The algorithm accurately determines analyte concentrations in complex matrices (seawater, food).
• It effectively compensates for matrix effects in high-dimensional data.
• Performance evaluation shows superiority over existing standard addition and direct multivariate methods.
• The algorithm demonstrates robustness against variations in signal-to-noise ratio (SNR) and matrix effect intensity.

Conclusions:

• The proposed algorithm offers a versatile solution for standard addition with high-dimensional data.
• It expands the applicability of standard addition to complex samples where blanks are unavailable.
• This method enhances the accuracy and reliability of quantitative analysis in challenging matrices.